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ABSTRACT This study tackles the problem of fault‐tolerant attitude estimation for small satellites. A probabilistic adaptive technique is presented for the multiplicative extended Kalman filter (MEKF) algorithm that is used in attitude estimation. The presented method is based on tracking the normalized measurement innovations in the filter and calculating the probability of the normal operation of the estimation system. Using this probability, the filter gain is corrected to maintain the tracking performance of the filter despite faulty measurements. In order to evaluate the performance of this method, several simulations are performed where different types of faults are introduced to the synthetic attitude sensor measurements (magnetometer and sun sensor) at different times. Simulation results are compared not only with a conventional EKF but also with another popular adaptive Kalman filter, an adaptive Kalman filter with multiple scaling factors (MSFs). [ABSTRACT FROM AUTHOR]

Indoor navigation framework plays a significant role in day today life. In the outdoor application, GPS signals identifies the exact location or points of the user, but it is not true in indoor scenarios. To overcome this issue there are various techniques have been devised to predict the position and identify the location of the user in the indoor application. These include attaching sensors to the shoes or suites or ceiling of the building and using WIFI signals to predict the position and navigate user to the destination. But not all the time this is possible as the sensors may get damaged and signal interferences may lead to large accumulated errors in the results. This paper proposes a novel technique to predict the position and navigate the user making use of the sensors present in the smartphone. Use of smartphone eliminates the need for external sensors to be attached to the shoes or suits, as it has inbuilt magnetometer, accelerometer, and gyroscope. The data generated by these sensors are used to estimate the pitch and roll values and also the heading information. The step count and the time at which the user performs each step are calculated using peak detection algorithm. A rule-based algorithm is proposed to estimate the step length and the smart phone beacon signals are used to provide the heading information. A voice based guiding facility is also built in to alert the user in case he selects the wrong path. With all these features, the proposed system certainly helps the user to navigate correctly from source location to the intended destination. The experiments are carried out in different scenarios under various realistic conditions and the results displays that, the proposed method achieves a high position accuracy with significant reduction in the error (less than 2.5%) and performs well compared to the conventional estimation methods. The performance is assessed in terms of displacement and root mean square error and compared with the position-estimation method (Poulose in IEEE Access 7: 11165–11177, 2019). [ABSTRACT FROM AUTHOR]

With the increasing number of households owning pets, the importance of sensor data for recognizing pet behavior has grown significantly. However, challenges arise due to the costs and reliability issues associated with data collection. This paper proposes a method for classifying pet behavior using cleaned meta pseudo labels to overcome these issues. The data for this study were collected using wearable devices equipped with accelerometers, gyroscopes, and magnetometers, and pet behaviors were classified into five categories. Utilizing this data, we analyzed the impact of the quantity of labeled data on accuracy and further enhanced the learning process by integrating an additional Distance Loss. This method effectively improves the learning process by removing noise from unlabeled data. Experimental results demonstrated that while the conventional supervised learning method achieved an accuracy of 82.9%, the existing meta pseudo labels method showed an accuracy of 86.2%, and the cleaned meta pseudo labels method proposed in this study surpassed these with an accuracy of 88.3%. These results hold significant implications for the development of pet monitoring systems, and the approach of this paper provides an effective solution for recognizing and classifying pet behavior in environments with insufficient labels. [ABSTRACT FROM AUTHOR]

Magnetometry is one of the geophysical methods that has wide applications. In traditional magnetometry, one sensor so-called magnetometer is used for measurements. In order to obtain the gradient in a specific direction, mathematical derivation can be used. Alternatively, a more accurate method of obtaining the gradient is using two sensors which simultaneously measure the magnetic field. In this case, the gradient is obtained in the direction where the two sensors are placed relative to each other. To obtain the Full Magnetic Tensor Gradient (FMTG) of the Earth's magnetic field, although direction derivatives can be used, the more accurate method is to use four sensors that measure the Earth's magnetic field simultaneously. In this way, the FMTG matrix can be obtained. So far, Superconducting Quantum Interference Device (SQID) sensors have been used the most in obtaining the FMTG. But these sensors are very expensive and their accurate performance is in a small temperature range. By developing the Micro Electro Mechanical System (MEMS) sensors with their variety of applications, geophysicists are also becoming interested in using such sensors. In this study, one of the precise MEMS magnetometers has been selected. Although the MEMS sensors have low sensitivity compared to SQUID magnetometers, due to their availability, being cheap, and lightness, they are a suitable option for FMTG measurements. Although these sensors do not have the resolution of SQUID magnetometers and they cannot be used on small anomalies that have caused changes of less than 160 nT on the Earth's magnetic field, these kinds of sensors are able to measure the magnitude of the magnetic field in three perpendicular directions and have an acceptable sensitivity. In this study, four sensors of this type have been set up in a cross arrangement. In order to solve the problem of the low sensitivity of these sensors compared to SQUID sensors, the distance between the sensors was chosen to be one meter so that the gradient value would be larger enough. Then, a survey has been performed on an iron ore deposit near Jafar Khan village, Saqqz City, Kurdistan province of Iran. In order to validate elements of the FMGT matrix, they have been compared with the directional derivatives obtained from a sensor. This comparison shows that the signal-to-noise ratio of elements Gxx, Gyx, Gzx, Gzy, and Gzz have increased around zero, 4.79, 10.80, 83.83, and 8.46, respectively, compared to their corresponding directional derivatives. This issue shows the ability of MEMS sensors to capture the full tensor of the magnetic gradient. [ABSTRACT FROM AUTHOR]

The weak-value-amplification (WVA) effect, which is also called weak measurement, has been developed extensively in various sensing systems. Here, we report the actual realization of the WVA effect in spin-exchange relaxation-free atomic magnetometer system, wherein the slight separation of transverse momentum of the probe light is amplified by introducing orthogonal pre- and post-selection states. A differential detector is used to obtain the transverse position of the probe light accurately in real time. The weak coupling of the magneto-optical interaction with atoms will be reflected in the differential signal. The WVA effect is observed and demonstrated directly and a high sensitivity of 8 fT / H z is achieved. Also, we obtain the stable and distinct simulated magnetocardiography signal through our system. The present successful implementation of this probe method paves the way for further technical noise suppression and sensitivity improvement of quantum sensors. [ABSTRACT FROM AUTHOR]

• Abnormal magnetometer data need to be considered. • The existing algorithm is improved. • Establish a dynamic adaptive step size model and constrain the attitude angle. • Improved algorithm mitigates the shortcomings of the existing algorithm. • The attitude accuracy of inertial sensor is improved. Aiming at the problems of angle oscillation, slow convergence speed, and low accuracy of traditional algorithms in low cost inertial element attitude determination, this study proposes an improved attitude algorithm based on geomagnetic correction robust model assisted adaptive step gradient descent. Firstly, a robust ellipsoid fitting algorithm is proposed to correct the geomagnetic anomaly data. Then, an adaptive step size model is established based on the momentum gradient descent method, and the iterative optimization parameters are automatically adjusted according to the error function. At the same time, the dynamic limiting mean filter is used to constrain the attitude angle, to calculate the optimal attitude information of the sensor. Finally, the algorithm is verified by static and dynamic experimental data. The results show that the improved algorithm can effectively suppress the angle oscillation, optimize the attitude convergence effect, and improve the accuracy of low cost inertial sensor attitude calculation. [ABSTRACT FROM AUTHOR]

This report presents a design for a double-rod core fluxgate sensor. The design features two identical conventional fluxgate sensors with their excitation coils connected in an anti-parallel configuration while the pick-up coils are interconnected in the same orientation. The purpose of the sensor is to detect displacement in general machining machines. The sensor consists of an excitation coil and a pick-up coil wrapped around a core made of the high permeability amorphous alloy Metglas 2714A ribbon, which has low hysteresis and a highly saturated magnetic field. The cores are bar-shaped and precision-cut with double core layers to increase flux density. The sensor's high sensitivity of 206 mV/mT and low noise of 0.4 nT/sqrtHz@1 Hz were experimentally determined. In distance measurements, the device has a responsivity of 2.18 V/mm with an initial mounting position to the target of 3 mm. The results indicate that the proposed design is suitable for displacement applications. [ABSTRACT FROM AUTHOR]

Compact optically-pumped magnetometers (OPMs) are now commercially available with noise floors reaching 10 fT/Hz1/2. However, to be used effectively for magnetoencephalography (MEG), dense arrays of these sensors are required to operate as an integrated turn-key system. In this study, we present the HEDscan, a 128-sensor OPM MEG system by FieldLine Medical, and evaluate its sensor performance with regard to bandwidth, linearity, and crosstalk. We report results from cross-validation studies with conventional cryogenic MEG, the Magnes 3,600 WH Biomagnetometer by 4-D Neuroimaging. Our results show high signal amplitudes captured by the OPM-MEG system during a standard auditory paradigm, where short tones at 1000 Hz were presented to the left ear of six healthy adult volunteers. We validate these findings through an event-related beamformer analysis, which is in line with existing literature results. [ABSTRACT FROM AUTHOR]

A novel magnetostrictive thin film fiber sensor is presented which can be utilized in an interferometric architecture. The magnetostrictive fiber sensor utilizes novel fiber and thin film technology to achieve high sensitivity in the sub-nanotesla regime. The base sensor architecture utilizes optical fiber wound in a novel low stress flat coil resembling a record, coated with a thin magnetostrictive film. Two prototype variants were fabricated, incorporating FeCo sputtered films of different thicknesses on 4 μm polyimide jacket single mode fiber. The sensors were incorporated into a fiber optic interferometric measurement apparatus to characterize time-varying magnetic field sensitivity. Device results are presented which demonstrate sensitivity is a function of film thickness. The experimental data exhibited a two-order magnitude sensitivity improvement as the thin film thickness was doubled. Sensitivity projections are made based on film thickness. [ABSTRACT FROM AUTHOR]

The paper presents the novel concept of the magnetoelectric sensor constructed using the amorphous glass ribbon. Its output characteristics (voltage pattern), conditions of work and experimental results are presented. The novel construction allows for minimizing the demagnetizing field in the core of the sensor and linearization of the characteristics between the magnetic field and obtained voltage. Conducted experiments were aimed at determining the sensor operation in the presence of the constant magnetic field (HDC). The main concern of the tests was to verify the linear dependency between the HDC value and the amplitude of the output voltage. Next, the computer model representing the sensor behavior in the constant magnetic field is described. The model implements the parameter identification task based on the regression algorithms. The presented work shows that the proposed device can be used to measure the weak magnetic field and the dependency between the output signal amplitudes and the constant component in the measured magnetic field is approximately linear. This enables measurements of even weak fields. [ABSTRACT FROM AUTHOR]

It is demonstrated that the sensitivity of a standard Hall magnetometer can be increased from ~2.5 × 10–3 to ~8 × 10–7 G/Hz1/2. The increase in the sensitivity is achieved due to the use of a sharp jump of the magnetic response of an HTSC (YBa2Cu3O7 – x) epitaxial film located on the surface of a Hall transducer at the first thermodynamic critical magnetic field Hic1 of twins. To increase the sensitivity, linearity, and noise immunity of the magnetometer, the working point is shifted to the value of Hic1 of twins by applying a constant support magnetic field as well as a decaying oscillating local bias field. This total field directed perpendicular to the film surface also provides an increase in the spatial resolution of the magnetometer, which is determined by the linear size of twins in the YBCO film. This size reaches ~300 nm (with a decrease to 20 nm for the appropriate minimal size of the twins). The design of the magnetometer allows measurements of not only dc but also ac magnetic fields in the range from 10–7 to 10–3 Oe at a frequency of 1 kHz, while preserving the high performance of the magnetometer. With the use of high-frequency magnetic-field transducers, the magnetometer can operate in the frequency range of up to 100 kHz and detect both alternating and pulsed magnetic fields. [ABSTRACT FROM AUTHOR]

Foot-mounted inertial navigation with zero velocity update (ZUPT) is commonly used in pedestrian positioning. To suppress the accumulating position error owing to the low-cost inertial sensor, classical dual-foot pedestrian navigation method provides the compensation using the two feet's relative step length, whose values can be set an upper bound constraint or measured by ranging sensors. However, it suffers from low improvement and poor wearability. In this study, the two sensors are distributed on the toe and ankle of the same leg. The sensor-to-sensor position is unchangeable with the lower limb segments. This makes the uncoupled two subsystems be related. Through gait analysis, the step is divided into the stance and swing phases. For each phase, positions, velocities, and the related characteristics of the toe and the ankle are analyzed and modeled, respectively. A Kalman filter is constructed to fuse the position and velocity observations according to the geometric constraints and the foot vector. The Mahony filter by fusing the inertial sensor and magnetometer is adopted for the attitude improvement. Several experiments at low and high walking velocities on the plane and upstairs are carried out to verify the proposed method. The results show that compared with the conventional single-inertial measurement unit (IMU) ZUPT and dual-IMU two-foot constraint methods, the position and heading errors have been well suppressed. [ABSTRACT FROM AUTHOR]

Scalar optically-pumped magnetometers (OPMs) are being developed in small packages with high sensitivities. The high common-mode rejection ratio of these sensors allows for detection of very small signals in the presence of large background fields making them ideally suited for brain imaging applications in unshielded environments. Despite a flurry of activity around the topic, questions remain concerning how well a dipolar source can be localized under such conditions, especially when using few sensors. In this paper, we investigate the source localization capabilities using an array of scalar OPMs in the presence of a large background field while varying dipole strength, sensor count, and forward model accuracy. We also consider localization performance as the orientation angle of the background field changes. Our results are validated experimentally through accurate localization using a phantom virtual array mimicking a current dipole in a conducting sphere in a large background field. Our results are intended to give researchers a general sense of the capabilities and limitations of scalar OPMs for magnetoencephalography systems. [ABSTRACT FROM AUTHOR]

An indoor trajectory is the path of an object moving through corridors and stairs inside a building. There are various types of technologies that can be used to reconstruct the path of a moving object and detect its position. GPS has been used for reconstruction in outdoor environments, but for indoor environments, mobile devices with embedded sensors are used. An accelerometer sensor and a magnetometer sensor are used to detect human movement and reconstruct the trajectory on a single floor. In an indoor environment, there are many activities that will create the trajectory similar to an outdoor environment, such as passing along the corridor, going from one room to another, and other activities. We need to analyse trajectories to obtain the movement patterns, understand the most frequently visited places or paths used as well as the least frequented ones. Furthermore, we can utilize movement patterns to obtain a better building design and layout. The latest studies focus on reconstructing the trajectory on a single floor. However, actual indoor environments are comprised of multi-floors and multi-buildings. The purpose of this paper is to reconstruct a trajectory in an indoor multi-floor environment. We have conducted extensive experiments to evaluate the performance of our proposed algorithms in a campus building. The result of our experiment shows that the height of the building can be detected using a barometer sensor that gives an atmospheric pressure reading which is then transformed by setting the range value according to the number of floors, enabling the sensors to detect activity in a multi-floor building. The readings obtained from the magnetometer sensor can be used to reconstruct the trajectory similar to the real path based on the direction and degree of direction. The system accuracy in recognizing steps in a multi-floor building is about 84%. [ABSTRACT FROM AUTHOR]

Attitude information obtained from low-cost magnetic, angular rate, and gravity (MARG) sensors is prone to be deteriorated by disturbances such as external acceleration and magnetic anomaly in operational environments. To address the problem, a robust attitude estimation algorithm using low-cost MARG sensors is proposed. An error state Kalman filter (ESKF) is used for data fusing, in which the attitude errors are interpreted as a small rotation vector, and the gyro bias variation is also estimated. The nominal state and the error state are propagated with unit quaternion and rotation vector, respectively. An attitude correction is followed which combines the nominal and error parts by quaternion multiplication. The robustness of the proposed algorithm is embodied in resisting accuracy degradation of the attitude estimation caused by both external acceleration and magnetic anomalies. While external accelerations and magnetic disturbances are detected online, the accuracy reduction in attitude estimation is prevented by adaptively adjusting the related measurement noise covariance matrix. Besides, a two-step measurement update strategy is designed to guarantee that the roll and pitch update is separated from the yaw update. Various rotation and land vehicle dynamic tests have been conducted to validate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Self-calibrating an array of 3-D field sensors, such as three-axis magnetometers and accelerometers, requires estimation of two variable sets—each sensor’s intrinsic model that maps its input field to the corresponding measurement and each sensor’s coordinates relative to a common frame of reference within the array. In this work, we propose the first unified self-calibration method for arrays of same-type 3-D field sensors, which is robust to anomalous sensor measurements unlike previous algorithms. The method breaks down the array calibration task into three steps of more easily subproblems, first estimating the intrinsic variables of each sensor independently, second computing the sensor coordinates with respect to a common reference frame, and last refining both these intrinsics and orientations jointly to minimize physically meaningful sensor estimation errors. Each stage has been carefully designed to maintain robustness to anomalies without compromising estimation quality. The performance of our method is compared against other state-of-the-art algorithms on both simulation and real data from a magnetometer array and accelerometer array, demonstrating significant improvements in accuracy and precision of the estimated array variables in versatile real-world self-calibration environments. [ABSTRACT FROM AUTHOR]

Indoor magnetic map (IMM) based on magnetic field fluctuations can be used in map assisted indoor positioning and navigation, etc. However, many IMM mapping solutions are time-consuming, labor-intensive, and high-cost, which limit the development and application of IMM. To solve the issues of accuracy, efficiency, and cost of IMM mapping collaboratively, we absorbed the idea of simultaneous localization and mapping (SLAM) method, and proposed a human-interactive mapping method of IMM based on the low-cost magnetometer, accelerometer, and rate gyro, i.e., MARG sensors. First, we defined IMM as the structured data set with a series of position stamps and magnetic field fluctuations. Then, we proposed the concept of general sensory calibration source (GSCS) and related metrics for human-interactive activities which provide virtual information to eliminate the time-accumulated error of position stamps and assist in obtaining stable magnetic field fluctuations. Finally, we conduct a series of experiments to verify the performance of the proposed method. The test results show that the proposed method can not only meet the requirements of mapping accuracy but also significantly improve mapping efficiency and reduce mapping cost, which is superior to other state-of-the-art algorithms. Specifically, the significant results are as follows: the mapping accuracy is about 0.31 m, and the mapping efficiency is 1800 m$^2$ per hour, and the cost of the main module of IMM mapping does not exceed U.S. $10. [ABSTRACT FROM AUTHOR]

Dynamic variations and slow field drifts exceeding 1 nT can be a challenge for optically pumped magnetometers (OPM) used for magnetoencephalography in existing three layer magnetically shielded rooms. These drifts can drive the sensors into non-linearity or worse, saturate the sensors. Here we describe that a triple axis active shielding coil system enclosing the outside surfaces of the room is sufficient to allow OPM operation in the linear regime. Auditory evoked fields for two types of sensor placement show the typical bi-hemispheric activation and the dipolar field map on a single hemisphere. Since the OPMs measure two orthogonal components of the field the sensors can be oriented to measure the quasi-radial and a tangential component of the brain activation at the same time. Results show qualitatively the theoretically expected field patterns. [ABSTRACT FROM AUTHOR]

This paper proposes a novel quaternion-based attitude estimation method for land-vehicle applications by fusing the low-cost magnetic, angular rate, and gravity (MARG) sensors and global navigation satellite systems measured velocity (GNSS-V). A structure of three-layer cascaded indirect Kalman filter (IKF) is devised to minimize the interactions of heterogeneous error sources on attitude quaternion solutions. It sequentially fuses the Earth's gravity vector, magnetic vector, and GNSS-V vector pairs with quaternion state constraints from Euler angles in each local filter. To further ensure the robustness of quaternion-based attitude solution, the external acceleration term is adaptively compensated within a sliding window of GNSS-V information. Meanwhile, independent and cross check procedures for observation vectors are also established for excluding individual sensor fault by taking land-vehicle dynamics into account. It is of significant importance that, in this contribution, the GNSS measured velocity is fully utilized in all potential aspects of aiding attitude determination with MARG sensors. Real-world vehicular experiments are carried out to evaluate the performance of our proposed method. The presented results verify the validity and efficiency thus the developed method may be promising for implemention in land-vehicle applications. [ABSTRACT FROM AUTHOR]

The article presents the possibilities of using popular MEMS inertial sensors in the object tilt angle estimation system and in the system for stabilizing the vertical position of the balancing robot. Two research models were built to conduct the experiment. The models use microcontroller development board of the STM32F3 series with the Cortex-M4 core, equipped with a three-axis accelerometer, magnetometer and gyroscope. To determine the accuracy of the angle estimation, comparative tests with a pulse encoder were performed. [ABSTRACT FROM AUTHOR]

Background: The foot progression angle is an important measure used to help patients reduce their knee adduction moment. Current measurement systems are either lab-bounded or do not function in all environments (e.g., magnetically distorted). This work proposes a novel approach to estimate foot progression angle using a single foot-worn inertial sensor (accelerometer and gyroscope).Methods: The approach uses a dynamic step frame that is recalculated for the stance phase of each step to calculate the foot trajectory relative to that frame, to minimize effects of drift and to eliminate the need for a magnetometer. The foot progression angle (FPA) is then calculated as the angle between walking direction and the dynamic step frame. This approach was validated by gait measurements with five subjects walking with three gait types (normal, toe-in and toe-out).Results: The FPA was estimated with a maximum mean error of ~ 2.6° over all gait conditions. Additionally, the proposed inertial approach can significantly differentiate between the three different gait types.Conclusion: The proposed approach can effectively estimate differences in FPA without requiring a heading reference (magnetometer). This work enables feedback applications on FPA for patients with gait disorders that function in any environment, i.e. outside of a gait lab or in magnetically distorted environments. [ABSTRACT FROM AUTHOR]

The knee flexion-extension angle is an important variable to be monitored in various clinical scenarios, for example, during physical rehabilitation assessment. The purpose of this work is to develop and validate a sensor fusion system based on a knee sleeve for monitoring of physical therapy. The system consists of merging data from two inertial measurement units (IMUs) and an intensity-variation based Polymer Optical Fiber (POF) curvature sensor using a quaternion-based Multiplicative Extended Kalman Filter (MEKF). The proposed data fusion method is magnetometer-free and deals with sensors’ uncertainties through reliability intervals defined during gait. Walking trials were performed by twelve healthy participants using our knee sleeve system and results were validated against a gold standard motion capture system. Additionally, a comparison with other three knee angle estimation methods, which are exclusively based on IMUs, was carried out. The proposed system presented better performance (mean RMSE $< $ 3.3 $^\circ$ , LFM coefficients, $a_1$ = 0.99 $\pm$ 0.04, $a_0$ = 0.70 $\pm$ 2.29, $R^2$ = 0.98 $\pm$ 0.01 and $\rho _C$ $>$ 0.99) when compared to the other evaluated methods. Experimental results demonstrate the usability and feasibility of our system to estimate knee motion with high accuracy, repeatability, and reproducibility. This wearable system may be suitable for motion assessment in rehabilitation labs in future studies. [ABSTRACT FROM AUTHOR]

In this letter, we describe the design and operation of a magnetic detection system for marking lesions in human breasts that might be cancerous. The system is comprised of a thin cylindrical permanent magnet implanted in the breast to mark the lesion location. A handheld detector, covered by a sterilized sleeve, would be manipulated by the surgeon to detect the location of the magnet. Evenly spaced sensors in the neck of the detector are calibrated to mitigate the effects of external magnetic fields. The nominal signature of the embedded magnet is used to estimate the distance from the detector tip to the magnet. The distance is indicated both by a display panel and the pitch of an audible signal. Details of the design process and initial laboratory operation of this magnetic marker detection system are presented. [ABSTRACT FROM AUTHOR]

We fabricate elliptical planar Hall effect magnetometers with Permalloy thickness ranging between 25 and 200 nm. We study the thickness dependence of their equivalent magnetic noise by examining the effect of the layer thickness on the signal and noise including Joule heating contributions. Sensors with a thickness of 50 nm achieve equivalent magnetic noise as low as ∼24 pT/ √ Hz at 50 Hz and ∼36 pT/ √ Hz at 10 Hz, which are the best reported values for any type of magnetic sensor of similar or smaller size. These results are achieved without the use of magnetic flux concentrators, which helps to reduce the sensor volume while improving its spatial resolution and reducing the complexity and time of its production and, hence, its potential cost. We discuss different routes for further resolution improvements. [ABSTRACT FROM AUTHOR]

Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo‐ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5∘ in texting mode and 12∘ in pocket mode. [ABSTRACT FROM AUTHOR]

The results of two radiation test campaigns on a low-cost commercial off-the-shelf magnetometer are presented. The test setup and the total ionization dose (TID) levels studied were designed to meet the requirements of a mission to land on Europa. Based on the Europa Lander Science Definition Team report, instruments inside an aluminum vault at the surface of Europa would need to withstand TID of up to 300 krad(SI). In order to evaluate the performance of the PNI RM3100 magnetometer, nine separate sensors were irradiated at two different facilities during two separate campaigns and under different configurations, including passive and active tests. Of the nine sensors, seven survived the TID of 300 krad(SI) while the other two sensors started presenting failures after reaching 150 krad(SI). Post-irradiation tests showed that eight of the nine sensors continued to work without appreciable degradation after stopping exposure, while one sensor stopped working altogether. [ABSTRACT FROM AUTHOR]

In recent years, the price increase of 3He has triggered the search for alternative neutron detectors. One of the viable options is a scintillation-based neutron detector. Usually, photomultiplier tubes (PMTs) are used in these detectors for photodetection. However, the increase in performance requirements such as the operability in magnetic field and spatial resolution necessitates an advanced neutron detector. Therefore, we developed a detector prototype with an active area of 13 cm × 13 cm using silicon photomultipliers (SiPMs). As compared to PMTs, SiPMs offer more compactness, more robustness, and a lower sensitivity to magnetic field. The final detector is aimed to be used in the future at the TREFF instrument of the Heinz Maier-Leibnitz Zentrum (MLZ) in Garching, Germany, for neutron reflectometry (NR). First measurements were carried out at TREFF and at the dedicated detector test instrument V17 at BER-II of HZB in Berlin, Germany. In this study, we report the results for detection efficiency, gamma discrimination, 2-D position resolution, count rate, and detection linearity. [ABSTRACT FROM AUTHOR]

In recent years, quantum detector tomography (QDT) is widely used in many fields, such as non-Gaussian states preparation, quantum metrology, quantum communication and so forth. In this paper, we used QDT to completely characterize a photon-number-resolving detector (PNRD) based on a multi-pixel photon counter (MPPC) at 650 nm, which operated in continuous wave (CW) mode. Reconstructing the positive operator-valued measure (POVM) accurately by QDT could help us theoretically derived the operation performance of MPPC in quantum sensing with nitrogen-vacancy (NV) center ensemble. The reconstruction fidelity of MPPC is larger than 99.94%, which means that QDT is very reliable. Comparing with conventional silicon avalanche photodiode (Si-APD), the fluorescent contrast of optically detected magnetic resonance (ODMR) spectrum of NV center ensemble detected by MPPC could be significantly improved. Since its excellent linearity and outstanding photon-number-resolving capability, MPPC could be further used in the DC magnetometry of NV center ensemble. We can infer that the magnetic field sensitivity can be effectively improved by replacing Si-APD with MPPC. Reportedly, this is an extremely rare research on the application of MPPC in NV center magnetometry. [ABSTRACT FROM AUTHOR]

This paper is a nontrivial extension of prior work on rate and position table leveling and inertial and magnetic sensors calibration techniques. The table leveling is essential for a class of calibration techniques using known stimuli, herein referred to as classical techniques. Such techniques are effective for the calibration of inertial sensors as they usually lead to closed-form solutions and body frame calibrations. However, the classical techniques are negatively affected by table leveling and azimuth orientation errors or by changes in the direction of the reference field. These errors are collectively referred to as field alignment errors in this paper. The impact of field alignment errors is usually avoided by using reference-free calibrations which rely on satisfying the invariance of the field modulus constraint. This constraint works well for orthogonal sensor triads as it leads to unique solutions. There is a great body of work dedicated to studying such calibration techniques for orthogonal sensor triads. However, redundant sensor configurations have too many degrees of freedom to satisfy the invariance of the modulus constraint; therefore, require special techniques. This paper introduces a novel approach that can be used to produce body frame calibrations for skew redundant sensor configurations in the presence of field alignment errors. Unlike prior work, no simplifying assumptions are made on the redundant sensor configuration (e.g., multiple orthogonal triads). The new method is showcased for the dodecahedron configuration, one of the most complex skew redundant configurations. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in hand movements in various application fields. However, the human hand is too dexterous and only has limited space. Developing a proper method to monitor and capture hand motion is meaningful. Conventionally, an optical measurement method is widely used to capture hand gestures. Nevertheless, this method is costly and has the problem of line-of-sight shielding. In this paper, a sensor fusion algorithm is proposed to calculate a more accurate result by calculating acceleration, magnetic field strength, and angular speed from the inertial measurement unit (IMU). The core concept of this algorithm is based on the idea of feedback control. To validate the performance, the active rotary platform is set up to compare the measured results to the references directly. Most of the error results are less than 3°, and the standard deviations are less than 1°. The measurement results ensure the feasibility of the inertial measurement method with the sensor fusion algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, an overview of the current status of high-Tc superconducting quantum interference devices (SQUIDs), from device engineering to biomagnetic applications, is given. The authors offer a description of the current status of SQUID sensors, challenges encountered, and the solution of fabricating SQUID sensors with low flux noises. The current challenge that we face is to fabricate high-Tc SQUIDs that are not only more reproducible than the current technology but also capable of providing a high IcRn product and fabricating SQUID with high yield. Improvement of flux noises and fabrication yield in the integrated multichoices directly coupled SQUID magnetometer or gradiometer with series SQUID array are presented. High-Tc SQUID magnetometers exhibiting magnetic field sensitivity of ∼30–50 fT/Hz1/2 or better at 100 Hz was demonstrated by incorporating serial SQUID into the pickup loop of the magnetometers. New technologies currently being developed and applications for high-Tc SQUIDs are addressed. [ABSTRACT FROM AUTHOR]

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In order to further improving noise performance and achieving higher spatial resolution for bio-magnetic sensing, there is a tendency to develop a multi-channel integrated system for micro magnetic sensors. In this study, we have developed a high resolution programmable oversampling MI sensor system, as a unit module for multi-channel integrated bio-sensor system, based on Pk-pk VD-type MI magnetometer. We have achieved a high field detection sensitivity with good linearity, and a noise level lower than 1 pT/Hz1/2, by utilizing the time-differential measurement in each MI element for suppressing the low frequency noise components and increasing the stability of sensor system. Meanwhile, the proposed MI sensor system can be easily enlarged into multi-channel system, and is more suitable for integration and mass production. Furthermore, we have successfully achieved the real time R peak and T wave measurements of magnetocardiography (MCG) via new MI sensor system, with a high real time signal-to-noise ratio (SNR) of about 20 dB. We have at the first time achieved the clear P wave measurements of MCG using MI sensor, in an unshielded environment. The real time MCG measurements via new MI sensor system at room temperature, in an unshielded environment, will contribute to the diagnosis of heart disease. [ABSTRACT FROM AUTHOR]

Accidental falls have become one of the most frequent general health issues in recent years due to the rate of occurrence. Individuals aged above 65 are more prone to accidental falls. Accidental falls result in severe injuries such as concussion, head trauma, physical disabilities even to deaths in serious cases if the patients are not rescued in time. Thus, researchers are focusing on developing fall detection systems that facilitate the detection and quick rescue of fall victims. The smartphone-based fall detection systems use various built-in sensors of smartphones mostly Tri-axial accelerometer, magnetometer, gyroscope, and camera. The majority of the systems employ threshold based algorithms (TBA). Some systems use machine learning (ML) based algorithms or a combination of ML and TBA based algorithms to detect falls. Each of these types of systems has its trade-offs. The goal of this paper is to review fall detection systems based on data from smartphone sensors that employ either one of TBA, ML or combination of both. We also present the taxonomy based on systematic comparisons of existing studies for smartphone-based fall detection solutions. [ABSTRACT FROM AUTHOR]

In the last decade, there is a significant progression and huge demand in using technology; specifically, those technologies are embedded in smartphones (SP). Examples of these technologies are embedding various sensors for multi-purposes. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant technologies. Besides this, indoor positioning services on smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; billing, shopping, security and safety, indoor navigation, entertainment applications, and other point-of-interest (POI) applications. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to existing accumulated errors of the embedded sensors. To solve this issue, this article proposes a hybrid multi-sensors measurement to reduce the existing sensors drifts and errors and to increase estimated heading accuracy of the smartphone. Further, the sensors' measurements with the previously estimated position are fused by using KALMAN Filter to determine the current location of the smartphone in each step of walking with better angular displacement accuracy. Proposed algorithm depends on increasing estimated angular displacement of the smartphone using combination of the integrated sensors' measurements. The achieved positioning accuracy through the proposed approach and based on trial experiments is around 2 meters, which is equivalent to 10% improvement in comparison with state of the art. [ABSTRACT FROM AUTHOR]

Purpose: The magnetometer measurement update plays a key role in correcting yaw estimation in fusion algorithms, and hence, the yaw estimation is vulnerable to magnetic disturbances. The purpose of this study is to improve the ability of the fusion algorithm to deal with magnetic disturbances. Design/methodology/approach: In this paper, an adaptive measurement equation based on vehicle status is derived, which can constrain the yaw estimation from drifting when vehicle is running straight. Using this new measurement, a Kalman filter-based fusion algorithm is constructed, and its performance is evaluated experimentally. Findings: The experiments results demonstrate that the new measurement update works as an effective supplement to the magnetometer measurement update in the present of magnetic disturbances, and the proposed fusion algorithm has better yaw estimation accuracy than the conventional algorithm. Originality/value: The paper proposes a new adaptive measurement equation for yaw estimation based on vehicle status. And, using this measurement, the fusion algorithm can not only reduce the weight of disturbed sensor measurement but also utilize the character of vehicle running to deal with magnetic disturbances. This strategy can also be used in other orientation estimation fields. [ABSTRACT FROM AUTHOR]

We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration drift using accelerometer and magnetometer data. This correction is computed, at each time instance, using a single gradient descent step with fixed step length. This fixed step length results in robustness against model errors, e.g., caused by large accelerations or by short-term magnetic field disturbances, which we numerically illustrate using Monte Carlo simulations. Our algorithm estimates a three-dimensional update to the orientation rather than the entire orientation itself. This reduces the computational complexity by approximately 1/3 with respect to the state of the art. It also improves the quality of the resulting estimates, specifically when the orientation corrections are large. We illustrate the efficacy of the algorithm using experimental data. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is universally acknowledged as an excellent tool to extract detailed spatial information with minimally invasive measurements. Efforts toward ultra-low-field (ULF) MRI are made to simplify the scanners and to reduce artifacts and incompatibilities. Optical atomic magnetometers (OAMs) are among the sensitive magnetic detectors eligible for ULF operation; however, they are not compatible with the strong field gradients used in MRI. We show that a magnetic-dressing technique restores the OAM operability despite the gradient, and we demonstrate submillimetric resolution MRI with a compact experimental setup based on an in situ detection. The proof-of-concept experiment produces unidimensional imaging of remotely magnetized samples with a dual sensor, but the approach is suited to be adapted for 3-D imaging of samples magnetized in loco. An extension to multisensor architectures is also possible. [ABSTRACT FROM AUTHOR]

• Wavelet width and sensing range angle are used to characterize the time-varying magnetic signature. • The signature peak strength, magnetometer's sensing range d and angle θ , are all independent of velocity. • The signature waveform width T is used as a cue, which has been found to be inversely proportional to the speed by d = vT. • The sensing range angle θ retains to be nearly θ y = 90° and θ x = 130° for y-axis and x-axis respectively, originating from constant d/CPA factors. A detection procedure by employing the induced magnetic signature characteristics for magnetic targets detection and identification is proposed in this paper. The highly sensitive tunneling magnetoresistive (TMR) sensor with low self-noise is adopted, while the magnetic target is traveling along a straight line at various speed and CPA (closest path approach) for magnetic anomaly detection (MAD). The triple-axis TMR sensor is characterized with a high sensitivity around 100 mV/V/Oe in a linear range of ±1 Oe and a self-noise of 170 pT/√Hz at 1 Hz. The signature waveform width and sensing range angle are used to analyze the signatures. It is found that the sensing range angle is independent of changing velocity and CPA. The proposed signature interpretation strategy is demonstrated by both experimental and simulated data. [ABSTRACT FROM AUTHOR]

Metallic magnetic calorimeters (MMCs) are highly linear cryogenic detectors that offer an excellent energy resolution, a signal rise time of <100 ns, and a high dynamic range. MMCs are of high interest for many experiments. One of them, i.e., the Electron Capture (EC) in 163Ho (ECHo) experiment, requires the utilization of large MMC detector arrays. The readout of such MMC arrays is a challenging task, which can be tackled using software-defined radios (SDRs). Although SDR is a well-known approach in communications engineering, a dedicated implementation for frequency division multiplexed readout of MMCs is new and one of the technological key elements of the ECHo project. ECHo will be the first experiment to use microwave superconducting quantum interference device (SQUID) multiplexed MMC detectors, and therefore, pioneering the hardware, firmware, and software development in this domain. This paper presents the detailed concepts and current status of the development. [ABSTRACT FROM AUTHOR]

Precision healthcare brings many technological challenges for the development of new tools. Among these, the detection of biomagnetic fields in the pico-Tesla range, at room temperature, requires the state-of-the-art sensors with high signal-to-noise ratio. Also, given that biomedical signals are characterized by their low frequency, minimizing the sensor noise has to be addressed simultaneously with the efforts to increase sensor sensitivity. In this paper, we maximize the spin-valve sensors sensitivity by optimizing the distance between the poles of double-layer magnetic flux concentrators (MFCs). An enhancement of the sensitivity from 1.5%/mT (3.8 mV/mT) to 654%/mT (1765 mV/mT) could be observed for a double-layer architecture when a distance from the recessed steep CoZrNb MFC to the NiFe pole is $100~\mu \text{m}$. The impact on the detectivity was evaluated through noise enabling the detection of magnetic fields of 322 pT/Hz1/2 at 10 Hz. [ABSTRACT FROM AUTHOR]

Brain activities have attracted considerable attention in medical and healthcare areas and engineering applications. Magnetoencephalogram (MEG) is a non-invasive technique for mapping and evaluating the functional activity of the brain. It is commonly measured by superconducting quantum interference devices (SQUIDs) which is the most reliable sensitive magnetometer for MEG measurement. However, the SQUIDs are still threatened by the need of cryogenic cooling liquids and a shielding room. For recording non-invasive cranial nerve activity, the technology with no liquid helium and high spatial resolution has been required for about 30 years. Sensitive micro-magnetic sensors referred to as magnetoimpedance (MI) sensors based on MI effect in thin amorphous magnetic wires are utilized as an electronics compass in a cellar phone. We have developed a gradiometer based on MI sensor for the purpose of biomagnetic field measurement. The gradiometer is composed of a pair of MI elements with 1 cm length each: a sensing element and a reference element with a baseline of 3 cm. The developed MI gradiometer has good linearity and a high sensitivity of $1.2 \times 105$ V/T with no amplification by semiconductor device. The noise level of the gradiometer is approximately 2 pT/Hz1/2. The MI sensor has system noise higher than SQUIDs, but it has considerable advantages that can measure closely to the scalp. Because of proximity measurement, the magnetic field from brain measured by MI sensor is larger than the case of SQUIDs. The precise MI gradiometer was tested as MEG detectors. The spontaneous brain activity (alpha rhythm) measurements were carried out on a female subject. As expected, the alpha rhythm signals simultaneously measured by electroencephalogram and MI gradiometer were significantly attenuated when the subject opens eyes and intensified with eyes closed. The subject’s $\alpha $ wave MEG signal has a main frequency component of 10–11 Hz and the maximum amplitude is about 25 pT (effective value). Significant susceptibility of MI sensor to spontaneous brain activity was confirmed by signal-to-noise ratio of 8/1. In addition, the measurement of evoked magnetic field N100 was carried out in the unshielded environment. The measurement results about N100 in this paper are almost consistent with the previous study by SQUIDs. [ABSTRACT FROM AUTHOR]

We have fabricated arrays of High-Tc Superconducting Quantum Interference Devices (SQUIDs) with randomly distributed loop sizes as sensitive detectors for Radio Frequency (RF) waves. These subwavelength size devices known as Superconducting Quantum Interference Filters (SQIFs) detect the magnetic component of the electromagnetic field. We used a scalable ion irradiation technique to pattern the circuits and engineer the Josephson junctions needed to make SQUIDs. Here, we report on a 300 SQUID series array with the loop area ranging from 6 to 60 μm2, folded in a meander line covering a 3.5 mm × 120 μm substrate area, made out of a 150 nm thick YBa2Cu3O7 film. Operating at a temperature of T = 66 K in an unshielded magnetic environment under low DC bias current (I = 60 μA) and a DC magnetic field (B = 3 μT), this SQIF can detect a magnetic field of a few picoteslas at a frequency of 1.125 GHz, which corresponds to a sensitivity of a few hundreds of fT / Hz and shows a linear response over 7 decades in RF power. This work is a promising approach for the realization of low dissipative subwavelength gigahertz magnetometers. [ABSTRACT FROM AUTHOR]

The availability of built-in sensors in mobile devices have paved a way for researchers to accurately determine human activities through these sensors. In this paper, we present a novel action recognition system based on sparse representation wherein, eight different human activities were classified. Our proposed classifier employs data of accelerometer, gyroscope, magnetometer and orientation sensor equipped in smartphones for recognizing human activities. Time-domain and frequency-domain features are derived from the acquired sensor data. We have introduced a novel algorithm for fusing the data from the four sensors using a sparse representation based technique that aid in achieving the best classification performance. In the proposed algorithm, if the majority of the sensors indicate a particular class as the output, then that specific class is assigned as the actual test class. However, if there is a disagreement between the classified output of different sensors, then a novel weighted fusion scheme is introduced to fuse the scores and the residue produced by different sensors. The weight used in fusion is chosen to be the standard deviation of the score vector. Thus, the features of excellent sensors are made to bestow more on to the result of action recognition. Finally, the action label is recognized based on an activity metric that maximizes the score while minimizing the residue. The performance analysis of the proposed system is performed using leave-one-subject-out approach. Performance evaluation metrics like recall, precision, specificity, F-score and accuracy are utilized in projecting the performance of the proposed system. It was shown that the proposed system attained a high overall accuracy of about 97.13%. [ABSTRACT FROM AUTHOR]